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# -*- coding: utf-8 -*-
"""newxyz.ipynb
Automatically generated by Colab.
Original file is located at
https://colab.research.google.com/drive/1mrPR47TT8REoppRDIPXFguM9d6Ho7Eea
"""
!pip install emoji textblob transformers langdetect googletrans deep-translator --quiet
import pandas as pd
import numpy as np
import nltk
from textblob import TextBlob
import emoji
import matplotlib.pyplot as plt
import seaborn as sns
import warnings; warnings.simplefilter('ignore')
from transformers import pipeline
from langdetect import detect
from googletrans import Translator
from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score,ConfusionMatrixDisplay, confusion_matrix
df_emoji = pd.read_csv("Emoji_Sentiment_Data.csv",usecols = ['Emoji', 'Negative', 'Neutral', 'Positive'])
df_emoji.head(3)
polarity_list = []
for index, row in df_emoji.iterrows():
polarity = 0
arg_1 = row['Positive'] > row['Negative']
arg_2 = row['Positive'] == row['Negative'] and row['Neutral'] % 2 != 0
if arg_1 or arg_2:
polarity = 1
polarity_list.append(polarity)
new_df_emoji = pd.DataFrame(polarity_list, columns=['sentiment'])
new_df_emoji['emoji'] = df_emoji['Emoji'].values
new_df_emoji.head(3)
text = "i ❤ sentiments #goodlife @today"
def extract_text_and_emoji(text = text):
global allchars, emoji_list
remove_keys = ('@', 'http://','https://', '&', '#', '*', '$', '^', '{', '(', '|')
clean_text = ' '.join(txt for txt in text.split() if not txt.startswith(remove_keys))
print(clean_text)
allchars = [str for str in text]
emoji_list = [c for c in allchars if c in emoji.EMOJI_DATA]
clean_text = ' '.join([str for str in clean_text.split() if not any(i in str for i in emoji_list)])
clean_emoji = ''.join([str for str in text.split() if any(i in str for i in emoji_list)])
return (clean_text, clean_emoji)
allchars, emoji_list = 0, 0
(ct, ce) = extract_text_and_emoji()
print('\nAll Char:', allchars)
print('\nAll Emoji:',emoji_list)
print('\n', ct)
print('\n',ce)
def detect_and_translate2(text):
translator = Translator()
lang = translator.detect(text).lang
trans = translator.translate(text, src=lang, dest='en')
return trans.text
# Load pre-trained sentiment analysis pipeline
sentiment_pipeline = pipeline('sentiment-analysis')
def get_sentiment(s_input='i hate sentiment analysis'):
results = sentiment_pipeline(s_input)
pred_senti = results[0]['label']
if pred_senti == 'POSITIVE':
pc = 1
else:
pc = 0
return pc
print(get_sentiment())
def get_emoji_sentiment(emoji_ls = '❤❤', emoji_df = new_df_emoji):
emoji_val_ls = []
for e in emoji_ls:
get_emo_senti = [row['sentiment'] for index, row in emoji_df.iterrows() if row['emoji'] == e]
if get_emo_senti: # Check if the list is not empty
emoji_val_ls.append(get_emo_senti[0])
else:
emoji_val_ls.append(0) # Or any default value you prefer
return emoji_val_ls
ges = get_emoji_sentiment()
print('Sentiment value of each emoji:',ges)
def analyze_emotion(text):
blob = TextBlob(text)
sentiment = blob.sentiment
if sentiment.polarity > 0:
if sentiment.subjectivity > 0.5:
return "joy" # Happy
else:
return "surprise" # Surprise
elif sentiment.polarity < 0:
if sentiment.subjectivity > 0.5:
return "anger" # Anger
else:
return "sadness" # Sad
else:
return "neutral" # Neutral
tweet = "Had a terrible experience at the restaurant tonight. Never going back."
emotion = analyze_emotion(tweet)
print(f"Emotion: {emotion}")
def get_text_emoji_sentiment(input_test = 'i hate 😒 sentiment analysis'):
(ext_text, ext_emoji) = extract_text_and_emoji(input_test)
print(f'Extracted: "{ext_text}" , {ext_emoji}')
ttext=""
lang = detect(ext_text)
if lang!="en":
ext_text= detect_and_translate2(ext_text)
print(f'Translated: "{ext_text}"')
senti_text = get_sentiment(ext_text)
print(f'Text value: {senti_text}')
senti_emoji_value = sum(get_emoji_sentiment(ext_emoji, new_df_emoji))
print_emo_val_avg = 0 if len(ext_emoji) == 0 else senti_emoji_value/len(ext_emoji)
print(f'Emoji average value: {print_emo_val_avg}')
senti_avg = (senti_emoji_value + senti_text) / (len(ext_emoji) + 1)
print(f'Average value: {senti_avg}')
senti_truth = "Positive" if senti_avg >= 0.5 else "Negative"
emtext = analyze_emotion(ext_text)
print(f'Text Emotion: {emtext}')
return senti_truth
print(get_text_emoji_sentiment())
test_df = pd.read_csv("german.csv",usecols = ['text', 'label'])
test_df=test_df[test_df['label'] != 'neutral']
test_df['label'] = test_df['label'].apply(lambda x: 1 if x == 'positive' else 0)
test_df.head(6)
test_df = test_df[test_df['text'].str.split().str.len() > 2]
def detect_and_translate3(text):
if not text.strip():
return " "
translator = Translator()
try:
lang = detect(text)
if lang != 'en':
trans = translator.translate(text, src=lang, dest='en')
return trans.text
except:
pass
return text
def preprocess_text(text):
text = text.lower()
text = ' '.join(word for word in text.split() if not word.startswith( ('@', 'http://','https://','+','-', '&', '#', '*', '$', '^', '{', '(', '|')))
text = ' '.join([word for word in text.split() if not any(i in word for i in emoji.EMOJI_DATA)])
text = detect_and_translate3(text)
return text if text else " "
test_df['text'] = test_df['text'].apply(preprocess_text)
def get_sentiment(text):
#text= detect_and_translate3(text)
result = sentiment_pipeline(text)[0]['label']
return 1 if result == 'POSITIVE' else 0
test_df['predicted_sentiment'] = test_df['text'].apply(get_sentiment)
y_true = test_df['label']
y_pred = test_df['predicted_sentiment']
accuracy = accuracy_score(y_true, y_pred)
precision = precision_score(y_true, y_pred)
recall = recall_score(y_true, y_pred)
f1 = f1_score(y_true, y_pred)
print("Accuracy ", accuracy)
print("Precision ", precision)
print("Recall ", recall)
print("F1 Score ", f1)
eg=[80.86,82.78,77.93,80.28]
fr=[58.27,62.37,41.72,0.5]
gm=[]
ab=[]
jp=[]
it=[]
from sklearn.metrics import confusion_matrix
import matplotlib.pyplot as plt
cm = confusion_matrix(y_true, y_pred)
disp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=['Negative', 'Positive'])
fig, ax = plt.subplots(figsize=(8, 6)) # Adjust figure size as needed
disp.plot(ax=ax, cmap='Blues', values_format='d')
plt.title('Confusion Matrix', fontsize=16) # Increase title font size
plt.xlabel('Predicted Label', fontsize=12) # Increase x-axis label font size
plt.ylabel('True Label', fontsize=12) # Increase y-axis label font size
plt.xticks(fontsize=16) # Increase x-axis tick label font size
plt.yticks(fontsize=16) # Increase y-axis tick label font size
plt.show()